The blood flow in the human cardiovascular system obeys the laws of fluid mechanics. Investigation of the flow properties reveals that a correlation exists between the velocity in time and space. The possible changes in velocity are limited, since the blood velocity has a continuous profile in time and space. This paper presents a new estimator (STC-MLE), which incorporates the correlation prop...

The asymptotic properties of four estimators for nonlinear mixed-e¤ects models are investigated: maximum likelihood estimator (MLE), an estimator based on the ...rst-order approximation to the expectation function (Vonesh and Carter (1992)), the two-stage, and the Lindstrom-Bates (1990) estimators. Two asymptotic situations are considered: (i) when N ! 1 and ni is ...nite, (ii) N ! 1 and minni ...

In regression problems involving vector-valued outputs (or equivalently, multiple responses), it is well known that the maximum likelihood estimator (MLE), which takes noise covariance structure into account, can be significantly more accurate than the ordinary least squares (OLS) estimator. However, existing literature compares OLS and MLE in terms of their asymptotic, not finite sample, guara...

Despite the fact that control charts are able to trigger a signal when a process has changed, it does not indicate when the process change has begun. The time difference between the changing point and a signal of a control chart could cause confusions on the sources of the problems. Knowing the exact time of a process change would help to reduce the time for identification of the special cause....

In this paper, we present a new maximum likelihood estimator (MLE) of carrier frequency offset for multicarrier signals in a frequency-selective Rayleigh fading channel. The proposed MLE is able to achieve wider estimation range of frequency offset with higher accuracy than previous blind approaches by exploiting the intrinsic structure of multicarrier signals. Simulations show that the estimat...

We investigate a subclass of exponential family graphical models of which the sufficient statistics are defined by arbitrary additive forms. We propose two l2,1norm regularized maximum likelihood estimators to learn the model parameters from i.i.d. samples. The first one is a joint MLE estimator which estimates all the parameters simultaneously. The second one is a node-wise conditional MLE est...

Delft University of Technology and Vrije Universiteit Amsterdam, University of Washington and University of Washington We study nonparametric estimation of the sub-distribution functions for current status data with competing risks. Our main interest is in the nonparametric maximum likelihood estimator (MLE), and for comparison we also consider the ‘naive estimator’ of Jewell, Van der Laan and ...

Using a Bewley-Hugget-Aiyagari model we show how to use the Fokker-Planck equation for likelihood inference in heterogeneous agent (HA) models. We study finite sample properties of maximum estimator (MLE) Monte Carlo experiments using cross-sectional data on wealth and income. Kullback–Leibler divergence investigate identification problems that may affect inference. Unrestricted MLE leads consi...

By design a wavelet's strength rests in its ability to localize a process simultaneously in time-scale space. The wavelet's ability to localize a time series in time-scale space directly leads to the computational e ciency of the wavelet representation of a N N matrix operator by allowing the N largest elements of the wavelet represented operator to represent the matrix operator [Devore, et al....

In this study, the maximum likelihood estimation (MLE) and Bayes estimation are exploited to make interval estimation based on adaptive progressive TypeII censoring for the Burr Type-XII distribution. Explicit form for the parameters of Bayes estimator doesn’t exist, so, Markov Chain Monte Carlo (MCMC) method is used as approximation to find posterior mean under squared error loss function. Rea...